How MT cells analyze the motion of visual patterns
Abstract
Neurons in area MT (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual MT neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in MT. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Subjective VQA is impractical for nearly every application other than benchmarking automatic or objective VQA algorithms.
“Subjective VQA is impractical for nearly every application other than benchmarking automatic or objective VQA algorithms.”
From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [2]
The models in [58], [72] are rather elaborate, physiologically plausible mechanisms designed to match the properties of visual neurons, while our model is designed from an engineering standpoint of capturing distortions in videos.
“The models in [58], [72] are rather elaborate, physiologically plausible mechanisms designed to match the properties of visual neurons. Our model is designed from an engineering standpoint of capturing distortions in videos.”
From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [72]
The temporal quality computation bears several similarities with the neuronal model of MT, including the weighting procedure based on distance between linear filters and the motion plane and the normalization of weighted responses.
“Our temporal quality computation bears several similarities with the neuronal model of MT in [58], [72]. Similarities include the weighting procedure based on the distance between the linear filters and the motion plane and the normalization of weighted responses.”
From Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010) · cites this paper as [72]
Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
Maunsell, J., & van Essen, D. (1983) · The Journal of Neuroscience · doi:10.1523/jneurosci.03-12-02563.1983 · Cited by 1,602 · see citing works
Neuronal Adaptation to Visual Motion in Area MT of the Macaque
Kohn, A., & Movshon, J. A. (2003) · Neuron · doi:10.1016/s0896-6273(03)00438-0 · Cited by 353 · see citing works
Self-citationCortical neurons: Isolation of contrast gain control
Geisler, W. S., & Albrecht, D. G. (1992) · Vision Research · doi:10.1016/0042-6989(92)90196-p · Cited by 211 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906 · see citing works
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621 · see citing works
Cognition does not affect perception: Evaluating the evidence for “top-down” effects
Firestone, C., & Scholl, B. J. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x15000965 · Cited by 1,200 · see citing works
Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos
Seshadrinathan, K., & Bovik, A. C. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2034992 · Cited by 708 · see citing works
A Normalization Model of Attentional Modulation of Single Unit Responses
Lee, J., & Maunsell, J. H. R. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004651 · Cited by 257 · see citing works
Representation of Concurrent Stimuli by Population Activity in Visual Cortex
Busse, L., Wade, A. R., & Carandini, M. (2009) · Neuron · doi:10.1016/j.neuron.2009.11.004 · Cited by 247 · see citing works
Neurons in monkey visual area V2 encode combinations of orientations
Anzai, A., Peng, X., & Van Essen, D. C. (2007) · Nature Neuroscience · doi:10.1038/nn1975 · Cited by 244 · see citing works
The neural basis of tactile motion perception
Pei, Y.-C., & Bensmaia, S. J. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00391.2014 · Cited by 77 · see citing works
Nonlinear circuits for naturalistic visual motion estimation
Fitzgerald, J. E., & Clark, D. A. (2015) · eLife · doi:10.7554/elife.09123 · Cited by 52 · see citing works
A Neural Representation of Naturalistic Motion-Guided Behavior in the Zebrafish Brain
Yildizoglu, T., Riegler, C., Fitzgerald, J. E., & Portugues, R. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.043 · Cited by 34 · see citing works
- Authors
- Rust, N. C., Mante, V., Simoncelli, E. P., & Movshon, J. A.
- Affiliations
- Howard Hughes Medical Institute (US)
- University of Zurich (CH)
- New York University (US)
- Corresponding author
- Rust, Nicole C
- Venue
- Nature Neuroscience
- Published
- 2006-10-15
- Volume
- 9
- Issue
- 11
- Pages
- 1421-1431
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2097167689
- Cited by
- 516
- References
- 5
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 13 · 2023: 25 · 2022: 15 · 2021: 16 · 2020: 24 · 2019: 33 · 2018: 17 · 2017: 26 · 2016: 32 · 2015: 30 · 2014: 42 · 2013: 28 · 2012: 37
- FWCI
- 14.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (R01EY002017, R01EY004440, EY 02017)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- direction selectivity, plaid, normalization, visual motion processing
- MeSH terms
- Algorithms, Animals, Macaca fascicularis, Macaca nemestrina, Models, Neurological, Motion Perception, Neurons, Afferent, Photic Stimulation, Visual Cortex, Linear Models, Nonlinear Dynamics, Pattern Recognition, Physiological
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
